Monograph on learning with noisy labels by MIT Press. Coming in 2024!

Tongliang Liu has published more than 100 papers at leading ML/AI conferences and journals, and received best paper awards (or nominations) at IJCAI 2017, ICME 2019, PacificVis 2021.
He has been widely recognised by his research. For example, he was ranked among the Best Rising Stars of Science in Australia by Research.com in 2022; he was ranked among the Global Top Young Chinese Scholars in AI by Baidu Scholar in 2022;
he was named in the Early Achievers Leaderboard by The Australian in 2020. Tongliang received the ARC DECRA Award in 2018, ARC Future Fellowship Award in 2022, and IEEE AI's 10 to Watch Award in 2023.
He also received multiple faculty awards from OPPO and Meituan.
Tongliang is also very proud of his talented students, who have made/are making/will make significant contributions to advancing science. They have also been recognised by many awards, e.g., Google PhD Fellowship Awards.

Tongliang Liu is currently working as an Associate Professor in Machine Learning with Mohamed bin Zayed University of Artificial Intelligence, United Arab Emirates. He is also the Director of Sydney AI Centre and a Senior Lecturer at University of Sydney, Australia; a Visiting Professor of University of Science and Technology of China, Hefei, China; a Visiting Scientist of RIKEN AIP, Tokyo, Japan.

His research interests lie in providing mathematical and theoretical foundations to justify and understand machine learning models and designing efficient learning algorithms for problems in the field of trustworthy machine learning, with a particular emphasis on

Learning with noisy labels,

Deep adversarial learning,

Causal representation learning,

Deep transfer learning,

Deep unsupervised learning,

Statistical deep learning theory.

He is regularly the meta-reviewer of ICML, NeurIPS, ICLR, UAI, IJCAI, and AAAI. He is the Action Editor of Transactions on Machine Learning Research, Associate Editor of ACM Computing Surveys, and in the Editorial Board of Journal of Machine Learning Research and the Machine Learning journal.

We are recruiting PhD and visitors. If you are interested, please send me your CV and transcripts.

Top News

5/2023, I accepted the invitation to serve as an Area Chair for AAAI 2024.

2/2023, I accepted the invitation to serve as an Area Chair for NeurIPS 2023.

12/2022, I accepted the invitation to serve as an Area Chair for ICML 2023.

12/2022, I accepted the invitation to serve as an Area Chair for UAI 2023.

09/2022, I was selected as an Australian Research Council Future Fellow (only three researchers across Australia was awarded in the field of Information and Computing Sciences in 2022).

09/2022, I was appointed as a Visiting Professor with University of Science and Technology of China.

09/2022, I was appointed as a Visiting Associate Professor with Mohammed Bin Zayed University of Artificial Intelligence.

08/2022, I accepted the invitation to serve as an Area Chair for ICLR 2023.

Selected Publications on Learning with Noisy Labels

Which is Better for Learning with Noisy Labels: The Semi-supervised Method or Modeling Label Noise?. [PDF]
Y. Yao, M. Gong, Y. Du, J. Yu, B. Han, K. Zhang, and T. Liu.
In ICML, 2023.

MSR: Making Self-supervised learning Robust to Aggressive Augmentations. [PDF]
Y. Bai, E. Yang, Z. Wang, Y. Du, B. Han, C. Deng, D. Wang, and T. Liu.
In NeurIPS, 2022.

Estimating Noise Transition Matrix with Label Correlations for Noisy Multi-Label Learning. [PDF]
S. Li, X. Xia, H. Zhang, Y. Zhan, S. Ge, and T. Liu.
In NeurIPS, 2022.

Class-Dependent Label-Noise Learning with Cycle-Consistency Regularization. [PDF]
D. Cheng, Y. Ning, N. Wang, X. Gao, H. Yang, Y. Du, B. Han, and T. Liu.
In NeurIPS, 2022.

Estimating Instance-dependent Bayes-label Transition Matrix using a Deep Neural Network. [PDF] [CODE]
S. Yang, E. Yang, B. Han, Y. Liu, M. Xu, G. Niu, and T. Liu.
In ICML, 2022.

Selective-Supervised Contrastive Learning with Noisy Labels. [PDF] [CODE]
S. Li, X. Xia, S. Ge, and T. Liu.
In CVPR, 2022.

Instance-Dependent Label-Noise Learning With Manifold-Regularized Transition Matrix Estimation. [PDF] [CODE]
D. Cheng, T. Liu, Y. Ning, N. Wang, B. Han, G. Niu, X. Gao, and M. Sugiyama.
In CVPR, 2022.

Rethinking Class-Prior Estimation for Positive-Unlabeled Learning. [PDF] [CODE]
Y. Yao, T. Liu, B. Han, M. Gong, G. Niu, M. Sugiyama, and Dacheng Tao
In ICLR, 2022.

Sample Selection with Uncertainty of Losses for Learning with Noisy Labels. [PDF] [CODE]
X. Xia, T. Liu, B. Han, M. Gong, J. Yu, G. Niu, and M. Sugiyama
In ICLR, 2022.

Me-Momentum: Extracting Hard Confident Examples from Noisily Labeled Data [PDF] [CODE] [Oral]
Y. Bai and T. Liu
In ICCV, 2021.

Instance-Dependent Label-Noise Learning under Structural Causal Models. [PDF] [CODE]
Y. Yao, T. Liu, M. Gong, B. Han, G. Niu, and K. Zhang.
In NeurIPS, 2021.

Understanding and Improving Early Stopping for Learning with Noisy Labels. [PDF] [CODE]
Y, Bai, E. Yang, B. Han, Y. Yang, J. Li, Y. Mao, G. Niu, and T. Liu.
In NeurIPS, 2021.

Provably End-to-end Label-noise Learning without Anchor Points [PDF] [CODE]
X. Li, T. Liu, B. Han, G. Niu, and M. Sugiyama
In ICML, 2021.

Class2Simi: A Noise Reduction Perspective on Learning with Noisy Labels [PDF] [CODE]
S. Wu*, X. Xia*, T. Liu, B. Han, M. Gong, N. Wang, H. Liu, and G. Niu
In ICML, 2021.

A Second-Order Approach to Learning with Instance-Dependent Label Noise. [PDF] [CODE] [Oral]
Z. Zhu, T. Liu, and Y. Liu.
In CVPR, 2021.

Robust early-learning: Hindering the memorization of noisy labels. [PDF] [CODE]
X. Xia, T. Liu, B. Han, C. Gong, N. Wang, Z. Ge, and Y. Chang.
In ICLR, 2021.

Part-dependent Label Noise: Towards Instance-dependent Label Noise. [PDF] [CODE] [Spotlight]
X. Xia, T. Liu, B. Han, N. Wang, M. Gong, H. Liu, G. Niu, D. Tao, and M. Sugiyama.
In NeurIPS, 2020.

Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning. [PDF] [CODE]
Y. Yao, T. Liu, B. Han, M. Gong, J. Deng, G. Niu, and M. Sugiyama.
In NeurIPS, 2020.

Learning with Bounded Instance- and Label-dependent Label Noise. [PDF] [CODE]
J. Cheng, T. Liu, K. Rao, and D. Tao.
In ICML, 2020.

Are Anchor Points Really Indispensable in Label-Noise Learning? [PDF] [CODE]
X. Xia, T. Liu, N. Wang, B. Han, C. Gong, G. Niu, and M. Sugiyama.
In NeurIPS, 2019.

Learning with Biased Complementary Labels. [PDF] [CODE] [Oral]
X. Yu, T. Liu, M. Gong, and D. Tao.
In ECCV, 2018.

Classification with Noisy Labels by Importance Reweighting. [PDF] [CODE] T. Liu and D. Tao. IEEE T-PAMI, 38(3): 447-461, 2015.

Phase-aware Adversarial Defense for Improving Adversarial Robustness. [PDF]
D. Zhou, N. Wang, H. Yang, X. Gao, and T. Liu.
In ICML, 2023.

Eliminating Adversarial Noise via Information Discard and Robust Representation Restoration. [PDF]
D. Zhou, Y. Chen, N. Wang, D. Liu, X. Gao, and T. Liu.
In ICML, 2023.

Modeling Adversarial Noise for Adversarial Defense. [PDF] [CODE]
D. Zhou, N. Wang, B. Han, and T. Liu.
In ICML, 2022.

Improving Adversarial Robustness via Mutual Information Estimation. [PDF]
D. Zhou, N. Wang, X. Gao, B. Han, X. Wang, Y. Zhan, and T. Liu.
In ICML, 2022.

Understanding Robust Overfitting of Adversarial Training and Beyond. [PDF] [CODE]
C. Yu, B. Han, L. Shen, J. Yu, C. Gong, M. Gong, and T. Liu.
In ICML, 2022.

Adversarial Robustness Through the Lens of Causality. [PDF] [CODE]
Y. Zhang, M. Gong, T. Liu, G. Niu, X. Tian, B. Han, B. Schölkopf, and K. Zhang
In ICLR, 2022.

Removing Adversarial Noise in Class Activation Feature Space [PDF] [CODE]
D. Zhou, N. Wang, C. Peng, X. Gao, X. Wang, J. Yu, T. Liu
In ICCV, 2021.

Towards Defending against Adversarial Examples via Attack-Invariant Features [PDF] [CODE]
D. Zhou, T. Liu, B. Han, N. Wang, C. Peng, and X. Gao
In ICML, 2021.

Dual-Path Distillation: A Unified Framework to Improve Black-Box Attacks. [PDF]
Y. Zhang, Y. Li, T. Liu, and X. Tian.
In ICML, 2020.

Selected Publications on Transfer Learning

Confident-Anchor-Induced Multi-Source-Free Domain Adaptation. [PDF] [CODE]
J. Dong, Z. Fang, A. Liu, G. Sun, and T. Liu.
In NeurIPS, 2021.

Domain Generalization via Entropy Regularization. [PDF] [CODE]
S. Zhao, M. Gong, T. Liu, H. Fu, and D. Tao.
In NeurIPS, 2020.

Transferring Knowledge Fragments for Learning Distance Metric from A Heterogeneous Domain. [Paper] [CODE]
Y. Luo, Y. Wen, T. Liu, and D. Tao. IEEE T-PAMI, 41(4): 1013-1026, 2019.

LTF: A Label Transformation Framework for Correcting Label Shift. [PDF] [CODE]
J. Guo, M. Gong, T. Liu, K. Zhang, and D. Tao.
In ICML, 2020.

Deep Domain Generalization via Conditional Invariant Adversarial Networks. [PDF] [CODE]
Y. Li, X. Tian, M. Gong, Y. Liu, T. Liu, K. Zhang, and D. Tao.
In ECCV, 2018.

Understanding How Feature Structure Transfers in Transfer Learning. [PDF] T. Liu, Q. Yang, and D. Tao.
In IJCAI, 2017.

Domain Adaptation with Conditional Transferable Components. [PDF] [CODE]
M. Gong, K. Zhang, T. Liu, D. Tao, C. Glymour, and B. Schölkopf.
In ICML, 2106.

Selected Publications on Statistical (Deep) Learning Theory

On the Rates of Convergence from Surrogate Risk Minimizers to the Bayes Optimal Classifier. [PDF]
J. Zhang, T. Liu, and D. Tao. IEEE T-NNLS, accepted 2021.

Control Batch Size and Learning Rate to Generalize Well: Theoretical and Empirical Evidence. [PDF]
F. He, T. Liu, and D. Tao.
In NeurIPS, 2019.

Algorithmic Stability and Hypothesis Complexity. [PDF] T. Liu, G. Lugosi, G. Neu and D. Tao.
In ICML , 2017.

Algorithm-Dependent Generalization Bounds for Multi-Task Learning. [Paper] T. Liu, D. Tao, M. Song, and S. J. Maybank. IEEE T-PAMI, 39(2): 227-241, 2017.